Navigating the Future of Tech Talent in an AI-Driven World with Clyde Seepersad | Open Source Summit NA 2025

Hey everybody. Welcome back to the Open Source Summit in Denver. And we're talking with Clyde Ersad, who's general manager of Linux training for the Linux Foundation or training in general across the board.

And they've got a new report out talking about what is the current state of tech talent. And so give us some of the highlights of that, some of the things that you found, and you've been doing this for a while, so what surprised you? Yeah, sure.

First off, thanks for having me, Mike. Always great to be able to get on platform and talk a little bit about the work that we're doing at LF Education. You know, we've been doing this for several years.

It used to be called the, uh, open source jobs report. And a few years ago we thought, you know, every job is an open source job in tech. So let's just broaden the lens a little bit.

And you know, this year of course, we focused in a little bit more keenly on the AI topic, which is sucking all the oxygen out the room. And we wanted to understand from practitioners, if you back away from the hype, what's really on their mind? And you know, it turns out what's really on their mind is this realization that I think the stat is like 96 or 97% of organizations see that there's potential significant value in ai.

A lot of them are realizing that you only to get that value unlocked, you have to go through your people and you have to cross-train and upskill your people because you can't insource, you can't outsource your AI strategy 'cause it's fundamentally about business processes. And so I think this, the impact that has is there's been a lot of stuff in the popular press around consumer for jobs, entry level developers and you know, what are the impacts gonna be? And that's true, but if you take a bigger lens picture of what's gonna happen and how the sort of deck chairs get rearranged, there are gonna be significant pockets where there's gonna be a need for need for more technical talent at the people level in order for organizations to unlock the potential value.

And organizations are realizing and saying, I think it's too ing and they need to invest more in, in technical talent in order to unlock ai. So, and I think we, we, we, uh, titled the report about something about the vibe versus the, the reality and, and part of the reality that the data is saying is as that first wave of enthusiasm and maybe panic has, has sort of washed over the real work of what is it gonna take to get there is coming into focus. And a lot of that's gonna have to be using the folks who we have who understand the organization, the processes, the customers, to figure out how we add these tools in to drive productivity.

You mentioned new jobs, new skills, new areas. Are there any that are becoming apparent to you where we might have new roles and new opportunities for people in the age of ai? Yeah, I think it's more the intersection of rules, right?

And so we already know from the DevOps revolution that it broke down the wall that we used to have between the dev dev side of the house and the engineering side of the house. I think what we're seeing with AI is it's breaking down the wall between the tech side of the house and the business side of the house. If you think about what it takes to build a really great agent, it's not all about the technology.

It's very deeply about what the process is and what the outcome is we're trying to achieve. And so I suspect what we're gonna see, yes, maybe fewer entry level jobs for software developer, because a lot of that stuff is, is quite good with the agenda coding and maybe more opportunities of people who come in With some technical talent, but also upskill on the business so that they understand how are we using these tools? What are these we using these tools to accomplish?

So I, I think the, the early read is there's gonna be more of these, um, I think the Tim is like T-shaped folks, right? They're deep in one area, but they understand across the business because that understanding across the business is gonna be important. 'cause you need that to unlock the power of what you can get with the, with the agent models.

So I think the nature of the rules, right, they rebecoming these broader rules because you're gonna need access to that broader set of things instead of like historically we've gotten super comfortable, right? I mean, you remember there was a time when you could train to be a database person and then do that for 30 years and then hang it up and leave and, and we've known that that's been gone for a while. Mm-hmm.

I think what we're seeing now is you kinda have to not just get across the different technical functions, but you have to start reaching out across into the business and really understanding that very deeply. And yet I still see DBAs out there. So who knows?

Listen, there are still QA folks out there, so you know, it's uh, it's a long tail To your point though, not just the way our jobs are gonna change, but the organization itself may change as as well. 'cause if the, all these silos become, um, fungible then, you know, the separation of roles between say sales and marketing and customer service and all these things that we've structured the companies around forever, might that also change in a way that the new job functions will be cross discipline to your point and the way the organization is structured needs to change too. I suspect.

That's right. And, and I was saying this in a talk yesterday. We're in an uncomfortable time right now because we all had playbooks that we were pretty comfortable with and we understood how they work.

And you came in at this level and you progressed to that level and there was career tracks and that's kind of gotten set on fire. And now we're all trying to collectively figure out what do the new playbooks look like in this sort of cross-functional T-shaped world. And I think the answer is we don't know.

And the fact that we don't know is creating a lot of anxiety, which is natural. I think part of the coaching we're trying to give folks is take a breath. It's okay to be anxious.

We're figuring out the new playbook. We have a sense that it's gonna be this multidisciplinary approach. I don't think anybody has figured out exactly what that looks like, but it's not, it's not the approach we were using three years ago, right?

It's gonna have to be something quite different when we think about how do we bring the power, you know, uh, the context about the business is not captured in any models and, but it's gonna be critical to unlocking the power or the models, right? Mm-hmm. So that new, the new playbooks are still very much being written.

Have you seen any generational divide here in how we're thinking about ai? Because I mean, I can talk to folks in their twenties and some of them will say, well this is great. AI will empower me to do all kinds of stuff that I previously would've had to have 10 years of experience to do.

Others in that same age bracket are saying, um, I'll never get a job in this field 'cause I can't break in. 'cause all the entry level tasks are now being automated and, and they feel a little bit stuck. On the other hand, the older folks are standing around going ranging from, you know, this is a conspiracy to eliminate my job to um, this is gonna be great because now I don't have to hire a bunch of entry-level minions to do stuff.

I can just have the AI agent do that for me. And it in their minds it will benefit them more. Yeah.

I think it, that is absolutely what we're seeing. I'll start on the first one. I do think there is a significant divide among the entry level folks coming in in terms of their tolerance for ambiguity.

The folks who can deal with ambiguity and uh, figure out kinda where they're gonna skate to and what they're passionate about are the first group of people you're talking about that are seeing it as opportunities. I think the folks who are anxious in the face of uncertainty because they want somebody to tell them exactly what to do are gonna have a lot of trouble. We are entering an age now where you have to be flexible, you have to be nimble.

And it's frustrating for young professionals because it used to be that you could get in and you can build up a certain level of competence and expertise and that then felt like a safe cushion to sit on. The reality is by the if as a slight overstatement, by the time you get to be an expert on something, it's no, it's probably no longer relevant. It's just moving so fast.

And so this idea of like never being comfortable in your mastery is a very different way of kind of existing in your career. And I, I, I think you and I are maybe further along so we probably won't have as much runaway to deal with it, but I tell my kids who are teenagers, you know, this is what's gonna be, you're gonna, you're gonna have to just be constantly evolving your skillset. And if you can wrap your head around that, you're gonna do fine.

If you are panicked by that, it's gonna be tough sweating. My, my son was on a job interview recently and he was talking about the job and I asked him, while you were worried about, you know, what AI might do to that particular field. And he, and he looked at me and he rolled his eyes and he said, dad, if I ain't gonna worry about AI all the time, I'll never get outta bed.

So Right. He's On the right track. Right.

And then I think from the, you know, mid-career and later professionals, there's also this disconnect between the folks that feel like they've earned their stripes. You know, they came up through, uh, they may be slightly annoyed at just how good the models are at at sort of digesting and analyzing the sort of state of knowledge. Uh, I suspect some of them are a little bit over indexed on, I'll never need to hire an entry level person again.

Uh, I I just had some, a plumber out to my house at $125 an hour. The guy is 62 and he said he never retired. 'cause he has more work than he can ever possibly do because there are no 20-year-old plumbers.

Right? If you break the on ramp of talent, like it's great for a while and then it stops, stops being great for the customers and it starts being great for the, for the folks who are left, We, we might have a robot for that jump. Yeah.

Well I'm not sure I feel about letting a robot mess with my plumbing, but that's a whole other question. Uh, but I think everybody's adjusting, right? And I think it's the, the folks who are in the middle of organizations are the ones caught in the pincher, right?

Because the board members are like, oh, this is great. You should triple your productivity. Just, just use the ai.

Of course there's no one thing that is ai. And then you've got the folks at the entry level. Some are panicked and some are excited and the folks at the, you know, in the middle of the org are the ones have to sort of bridge the gap between the two.

Yeah. Because I do feel like there's a certain amount of noise coming from the C-suite execs about, you know, we're gonna improve productivity, we're gonna reduce head count and all this other stuff. And, and as I listen to them, I can't help but wonder most of 'em are gonna wind up hiring people back.

'cause they don't really understand exactly how the thing that they're working through actually works. It Is gonna play out. You know, I saw a great stat the other day, uh, in the US in the 60 years following World War ii.

Productivity increased on average by two and a half percent a year. 4%. If you remember the hype back then turns out the internet made us less productive.

We, we somehow lost crowd. I think the AI tools have the potential to kinda get that productivity number back up. Uh, if done right, the the trick is how do you do it right?

Right. Doing it right is gonna require leveraging everybody in your organization because it is fundamentally about looking at your company and figuring out how can we use these tools to make better decisions faster? How can we use these tools to get stuff out to our customers that is better mapped to what their needs are and have more feedback?

Some of that's data, a lot of that is judgment and a lot of that is knowledge, right? And so figuring out the people part of that, I think Boston Consulting Group had this great framework out, they're calling it 10, 20 70 10 percents support the tools. 20% is about the processes, 70% is about the people.

We're super excited about the tools and everybody's talking about them. It's 10% of, of the challenge. Alright?

I mean I was was having this conversation with one of my neighbors and he was pointing out, they had bought a manufacturing facility in Vietnam 10 years ago and there were 300 people working at it and they have since tripled the output from that factory. But there's still 300 people working there. And his point was we increased productivity but we'd rather increase the productivity and the revenue.

And then it's not just about reducing headcount per se unless, you know, that may be a byproduct but maybe not the primary goal. Well you know, I don't think anybody can point to a company that ever shrank its weight to greatness. Mm-hmm It's about growth, right?

And it's about figuring out what more can we do. Yeah. So from your estimation, what is real in terms of the capabilities of AI copilots and now agents versus how much remains theoretical and what's the timeline spectrum in your mind?

Oh well the timeline's a tricky one. But I will say what's real is I have a old friend who has long time, uh, con uh, contact center manager and they have significantly reduced their tier one staffing outta the Philippines because a lot of the questions that people call in to ask for the typical things that will go to a tier one desk are actually really good to put intergenic model form because it's very well described transactions, right? I wanna reset my password, I forgot my login, I need a refund.

So there are all things like that where you've already seen pretty significant movement sort of in the real world. I think we're starting to see on the co-development side, uh, much faster velocities, right? Of people being able to, nobody enjoys the uh, mundane part of the coating where you're stitching services together.

I almost nobody. So if you can accelerate through that bit of sort of assembling the blocks of cool and you can spend more of your time thinking about what's the new functionality you're trying to create, I don't think you'll find a lot of people saying, oh no, I really like typing line by line, you know, of all that code. So I think that's real.

I think the productive, I think the velocity argument is real. There may be some people who are feeling like they could be comfortable with largely model generated code. I would argue most people are not.

And, and they, and they still want sort of human review and guidance in that process. Uh, so there's something real there. I think the trick is figuring out what does that look like?

Is it a velocity increase? It looks more like your factory example where you do more, uh, there's some opportunities to pair back on kind of what the, what the on ramp of talent looks like. The on ramp of talent doesn't go to zero because there's no business in the world and that can sustain that.

So I think there's some basic stuff. The more interesting thing is when you get into the more you know the meat of the organizations, right? Most things in organizations aren't as simple as reset my password.

You're talking about dealing with suppliers, you're talking about dealing with customers, you're talking about dealing with multiple departments across your own organization. There's a lot of context and a lot of process and a lot of organizational dynamics at play that's gonna take real work. And that's not work about is it MCP or A two A?

That's work about the horse training that happens within an organization as you reword processes. Uh, so it's not about the tools. The tools have a ton of potential, right?

They're very good at inference, they're very good at analyzing yesterday's data. They're not good at envisioning the future because they're trained on yesterday's data. So yeah, if you go back 20 years, one of these tools probably wouldn't suggest the iPhone because there was no data suggesting that.

You know, that sort of unique combination of factors, right? So innovation, a lot of the core innovation I think is the human spark layered on top of great analytics. The analytics piece the tools are exceptionally good at.

And To use your call center analogy, I'm not quite clear where those agents are gonna be 'cause I can see the call center people are gonna build their agents and they expect humans to interact with that. But I got news from them. My agent's just gonna scan their site to find what I want to have in the first place and bypass their agents.

It's gonna be very interesting to see how that one plays out. So, or maybe those two agents will talk to each other somehow or other and share something interesting. Clearly everybody who's hiring anybody's looking for people who have AI skills.

What's your best advice about how to go get those AI skills? Because I think everybody kind of nods their head and says yes AI skills but then they don't know where to put their arms around that. It's a great question Mike.

And it's something I've been saying for a couple of years now. Technical talent is something you should build, not buy. I think a lot of hiring managers got into this mindset that I'll hop on LinkedIn, I'll find some candidates, I'll hire somebody and it'll be great.

First off, that is a circular firing squad 'cause I poached from you, you poached from him. He POEs from me and we haven't added any new talent. Secondly, it is logically impossible to hire somebody with three years of fi agent AI model building.

And so this desire, we, you know, we always want to hire the person that trained up on somebody else's dime. You, You haven't seen that ad for 10 years of experience a AI agent. Yeah, Exactly right.

So I think everybody's having is realizing that the easy way out, which is to hire somebody that learned it somewhere else is a logical impossibility. And that leaves you with one choice. The people I have are gonna be the people that I have to rely on.

And so how do I systematically invest in training and upskilling those people? And you know, one of the things that was interesting in the research report was it wasn't just that people were saying AI skills, although that was the highest on the list was like some two foods. More than half of people said we're short on cybersecurity, we're short on cloud native basics.

You know, we're short on DevOps and pipelines and the mechanics have sort of pushed stuff through. And I think that's part of what we've been playing back to folks, is you can chase the hot sexy stuff to say, well we gotta do some A two A, we gotta do MCP. So we could build agents that talk to agents that manage other agents fundamentally the stuff is gonna run on the infrastructure we have, which is this sort of cloud native architecture.

It would behoove people to focus on making sure folks understand the basics. 'cause the abstractly, our tools are gonna keep coming super fast. They're gonna keep running in our data centers where Linux is the command line and Kubernetes is the orchestration and Prometheus is doing logging.

You know, it's, it's a learn to read and write approach, right? Of saying, listen, you don't know what this is gonna be, but we know what this is gonna be. Make sure folks are comfortable here and make sure you're exposing them to all the new stuff that's coming out.

'cause it's every week, you know this like it every single week more stuff comes out, but the base layer doesn't change. Yeah. Well we also know there's gonna be a lot more of this up here and this down here isn't gonna get that much bigger and better.

Yeah. It's not, it's not sexy, but it's what we run on, right? Right.

So making sure everybody knows what you run on feels like a pretty good strategy if you're gonna dip the business on, you know, this this new so family of, of technologies. So lemme ask you, who's responsible for training? And I'm asking this question because uh, if I go talk to some of the attendees, they'll say they work for certain companies 'cause they give them access to training, right?

Other companies expect the employee to kind of stay current and it's up to them and they to get that training somehow themselves. So how do you navigate those two extremes? Because it seems like, uh, both parties expect the other to be doing something.

You know, My argument to organizations is if you think as 97% of people do that, there's gonna be this significant win from implementing these technologies. And you realize that the only way you're gonna get that win is by relying on the people within your organization to figure out how to correctly use these tools. How to be privacy respecting how not to give away all your intellectual property, how to do it in a way that doesn't land with, you know, horrible backlash from from your customers.

If you think that's gonna be what's required. But on the other hand you say, well they should figure it out themselves. They're grown people.

That's probably not gonna end well for you, right? Like, you'd never do that in any other aspect of your business. You'd say this is critically important.

I want to have a clear plan on a strategy and alignment on how I'm doing this. Upskilling your technical talent is no different than any other strategic initiative, right? If you're, if you think it's strategic, you have to act like it's strategic.

And if companies don't and they instead say, well, but the people should be responsible for them themselves. I think what you're gonna start seeing is DV is the performance diverging between the organizations that take seriously the 10, 20, 70 rule and the organizations that are still suspicious that if people train up, they're really training up. 'cause they're trying to leave to move to, to some other place, right?

And so if this is your strategy, you should align your strategy. And if your strategy requires people who understand the tech and are comfortable working in it and you, and you don't make that a priority across your organization to do it consistently, I don't think you can expect it to be as successful as those who make that commitment. All right folks, you're heard in here wise, man once said, failing the plan is planning to fail.

Still true in the age of ai. Thanks for coming by. Thanks so much, Mike.

All.